Senior Data Scientist - ML, Python

Job Summary

Key Responsibilities-AIOPS Technical Lead:

Design, develop, and deploy end‑to‑end AI applications from data ingestion to production inference. Build data pipelines for data preparation, feature engineering, and model training. Select, train, evaluate, and optimize machine learning and deep learning models. Develop APIs and services to expose AI models for real‑time and batch use cases. Implement monitoring, logging, and model performance tracking in production. Collaborate with product, data, and domain teams to translate business requirements into AI solutions. Ensure AI solutions meet enterprise standards for security, scalability, and responsible AI usage. Required AI Skill Areas (Core – 4 Skills) 1. Machine Learning & Model Development Strong understanding of supervised and unsupervised learning techniques. Experience with model training, evaluation, and tuning. Ability to select appropriate algorithms based on use case and data characteristics. Familiarity with evaluation metrics and model validation techniques. 2. Data Engineering & Feature Engineering Hands‑on experience with data preprocessing, cleaning, and exploratory data analysis. Strong skills in feature engineering and handling real‑world data issues. Proficiency in working with structured and semi‑structured data from multiple sources. Experience using Python libraries such as Pandas and Num Py, along with SQL. 3. Generative AI / LLM‑Based Application Development Experience building applications using Large Language Models (LLMs). Strong skills in prompt design, prompt optimization, and template creation. Working knowledge of embeddings, vector search, and Retrieval‑Augmented Generation (RAG). Experience integrating LLM APIs into enterprise applications. 4. AI System Design, Deployment & MLOps Ability to design scalable AI architectures for training and inference. Experience deploying models as APIs or services (e.g., using FastAPI or Flask). Understanding of model versioning, monitoring, data drift, and retraining strategies. Familiarity with containerization and cloud deployment concepts. Technical Skills Strong proficiency in Python Experience with ML/DL frameworks such as Py Torch or Tensor Flow Familiarity with REST APIs, microservices, and cloud platforms (GCP) Knowledge of model lifecycle management tools (e.g., MLflow – preferred) Domain & Soft Skills Ability to work independently without code‑generation tools Strong analytical and problem‑solving skills Clear communication with technical and non‑technical stakeholders Healthcare Domain knowledge is a strong plus Nice to Have Experience with responsible AI practices and explainability Exposure to AI ethics, bias mitigation, and governan Experience working in regulated or enterprise environment Lea

Key Responsibilities

Skill Requirements

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Other Requirements

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